SoroushVahidi/scidocs huggingface dataset:
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
(2017)

Training convolutional networks (CNNs) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large networks that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new [7, 3], but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.
Local Naive Bayes Nearest Neighbor for image classification
(2012)

We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class's training descriptors, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor's local neighborhood. We show an increase in classification accuracy when we ignore adjustments to the more distant classes and show that the run time grows with the log of the number of classes rather than linearly in the number of classes as did the original. Local NBNN gives a 100 times speed-up over the original NBNN on the Caltech 256 dataset. We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features. We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model. However, we find that local NBNN, while competitive with, does not beat state-of-the-art spatial pyramid methods that use local soft assignment and max-pooling.
Bayesian Personalized Ranking with Multi-Channel User Feedback
(2016)

Pairwise learning-to-rank algorithms have been shown to allow recommender systems to leverage unary user feedback. We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. The feedback types are drawn from different "channels", in which users interact with items (e.g., clicks, likes, listens, follows, and purchases). We build on the insight that different kinds of feedback, e.g., a click versus a like, reflect different levels of commitment or preference. Our approach differs from previous work in that it exploits multiple sources of feedback simultaneously during the training process. The novelty of MF-BPR is an extended sampling method that equates feedback sources with "levels" that reflect the expected contribution of the signal. We demonstrate the effectiveness of our approach with a series of experiments carried out on three datasets containing multiple types of feedback. Our experimental results demonstrate that with a right sampling method, MF-BPR outperforms BPR in terms of accuracy. We find that the advantage of MF-BPR lies in its ability to leverage level information when sampling negative items.
When Morality Opposes Justice : Conservatives Have Moral Intuitions that Liberals may not Recognize
(2006)

Researchers in moral psychology and social justice have agreed that morality is about matters of harm, rights, and justice. On this definition of morality, conservative opposition to social justice programs appears to be immoral, and has been explained as a product of various non-moral processes such as system justification or social dominance orientation. In this article we argue that, from an anthropological perspective, the moral domain is usually much broader, encompassing many more aspects of social life and valuing institutions as much or more than individuals. We present theoretical and empirical reasons for believing that there are five psychological systems that provide the foundations for the world s many moralities. The five foundations are psychological preparations for detecting and reacting emotionally to issues related to harm/care, fairness/reciprocity, ingroup/loyalty, authority/respect, and purity/sanctity. Political liberals have moral intuitions primarily based upon the first two foundations, and therefore misunderstand the moral motivations of political conservatives, who generally rely upon all five foundations.
A Review on Data Mining in Banking Sector
(2013)

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification
(2012)

Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification ata mining unctional link artificial neural networks ulti-layer perception article swarm optimization mproved particle swarm optimization VM capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN = ISO − FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN). SN
AudioDAQ: turning the mobile phone's ubiquitous headset port into a universal data acquisition interface
(2012)

We present AudioDAQ, a new platform for continuous data acquisition using the headset port of a mobile phone. AudioDAQ differs from existing phone peripheral interfaces by drawing all necessary power from the microphone bias voltage, encoding all data as analog audio, and leveraging the phone's built-in voice memo application (or a custom application) for continuous data collection. These properties make the AudioDAQ design more universal, so it works across a broad range of phones including sophisticated smart phones and simpler feature phones, enables simple analog peripherals without requiring a microcontroller, requires no hardware or software modifications on the phone itself, uses significantly less power than prior approaches, and allows continuous data capture over an extended period of time. The AudioDAQ design is efficient because it draws all necessary power from the microphone bias voltage, and it is general because this voltage and a voice memo application are present on most mobile phones in use today. We show the viability of our architecture by evaluating an end-to-end system that can capture EKG signals continuously for hours and send the data to the cloud for storage, processing, and visualization.
Quasi-Newton Optimization in Deep Q-Learning for Playing ATARI Games
(2018)

Reinforcement Learning (RL) algorithms allow artificial agents to improve their selection of actions so as to increase rewarding experiences in their environments. The learning can become intractably slow as the state space of the environment grows. This has motivated methods that learn internal representations of the agents state by a function approximator. Impressive results have been produced by using deep artificial neural networks to learn the state representations. However, deep reinforcement learning algorithms require solving a non-convex and nonlinear unconstrained optimization problem. Methods for solving the optimization problems in deep RL are restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). The major drawback of the SGD methods is that they have the undesirable effect of not escaping saddle points. Furthermore, these methods require exhaustive trial and error to fine-tune many learning parameters. Using second derivative information can result in improved convergence properties, but computing the Hessian matrix for large-scale problems is not practical. QuasiNewton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives. The limitedmemory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach is one of the most popular quasi-Newton methods that construct positive definite Hessian approximations. In this paper, we introduce an efficient optimization method, based on the limited memory BFGS quasi-Newton method using line search strategy – as an alternative to SGD methods. Our method bridges the disparity between first order methods and second order methods by continuing to use gradient information to calculate a low-rank Hessian approximations. We provide empirical results on variety of the classic ATARI 2600 games. Our results show a robust convergence with preferred generalization characteristics, as well as fast training time and no need for the experience replaying mechanism.
Perforator flaps of the facial artery angiosome.
(2013)

For small to moderate-sized defects in the head and neck region, local flaps have been the mainstay of reconstruction for years. However, in certain instances, additional flap translation is required be it advancement, transposition or rotation. In such cases, the local flap concept is combined with perforator flap know-how, allowing larger loco-regional flaps to be raised to reconstruct relatively larger defects, even in cosmetically-expensive areas. In our cohort of fifteen patients', we have utilised detailed microanatomy of the facial artery perforators to reconstruct such defects with good results.
Quality assessment of Median filtering techniques for impulse noise removal from digital images
(2016)

Impulse noise still poses challenges in front of researchers today. The removal of impulse noise brings blurring which leads to edges being distorted and image thus being of poor quality. Hence the need is to preserve edges and fine details during filtering. The proposed method consists of noise detection and then removal of detected noise by Improved Adaptive Median Filter using pixels that are not noise themselves in gray level as well as colour images. The pixels are split in two groups, which are noise-free pixels and noisy pixels. In removing out Impulse noise, only noisy pixels are processed. The noiseless pixels are then sent directly to the output image. The proposed method adaptively changes the masking matrix size of the median filter based on the count of the noisy pixels. Computer simulation and analysis have been carried out eventually to analyse the performance of the proposed method with that of Simple Median Filter (SMF), Simple Adaptive Median Filter (SAMF) and Adaptive Switched Median Filter (ASMF). The proposed filter proves to be more efficient in terms of both objective and subjective parameters.
Correlation clustering in general weighted graphs
(2006)

We consider the following general correlation-clustering problem [N. Bansal, A. Blum, S. Chawla, Correlation clustering, in: Proc. 43rd Annu. IEEE Symp. on Foundations of Computer Science, Vancouver, Canada, November 2002, pp. 238–250]: given a graph with real nonnegative edge weights and a 〈+〉/〈−〉 edge labelling, partition the vertices into clusters to minimize the total weight of cut 〈+〉 edges and uncut 〈−〉 edges. Thus, 〈+〉 edges with large weights (representing strong correlations between endpoints) encourage those endpoints to belong to a common cluster while 〈−〉 edges with large weights encourage the endpoints to belong to different clusters. In contrast to most clustering problems, correlation clustering specifies neither the desired number of clusters nor a distance threshold for clustering; both of these parameters are effectively chosen to be the best possible by the problem definition. Correlation clustering was introduced by Bansal et al. [Correlation clustering, in: Proc. 43rd Annu. IEEE Symp. on Foundations of Computer Science, Vancouver, Canada, November 2002, pp. 238–250], motivated by both document clustering and agnostic learning. They proved NP-hardness and gave constant-factor approximation algorithms for the special case in which the graph is complete (full information) and every edge has the same weight. We give an O(log n)-approximation algorithm for the general case based on a linear-programming rounding and the “region-growing” technique. We also prove that this linear program has a gap of (log n), and therefore our approximation is tight under this approach. We also give an O(r3)-approximation algorithm for Kr,r -minor-free graphs. On the other hand, we show that the problem is equivalent to minimum multicut, and therefore APX-hard and difficult to approximate better than (log n). © 2006 Elsevier B.V. All rights reserved.
Leveraging Text and Knowledge Bases for Triple Scoring: An Ensemble Approach - The BOKCHOY Triple Scorer at WSDM Cup 2017
(2017)

We present our winning solution for the WSDM Cup 2017 triple scoring task. We devise an ensemble of four base scorers, so as to leverage the power of both text and knowledge bases for that task. Then we further refine the outputs of the ensemble by trigger word detection, achieving even better predictive accuracy. The code is available at https://github.com/wsdm-cup-2017/bokchoy.
Compact Nonlinear Maps and Circulant Extensions
(2015)

Kernel approximation via nonlinear random feature maps is w idely used in speeding up kernel machines. There are two main challenges for the conventional kernel ap proximation methods. First, before performing kernel approximation, a good kernel has to be chosen. Pickin g a good kernel is a very challenging problem in itself. Second, high-dimensional maps are often required i n order to achieve good performance. This leads to high computational cost in both generating the nonlinear ma ps, nd in the subsequent learning and prediction process. In this work, we propose to optimize the nonlinear m ps directly with respect to the classification objective in a data-dependent fashion. The proposed approa ch achieves kernel approximation and kernel learning in a joint framework. This leads to much more compact maps wit hout hurting the performance. As a by-product, the same framework can also be used to achieve more compact ke rnel maps to approximate a known kernel. We also introduce Circulant Nonlinear Maps, which uses a circu lant-structured projection matrix to speed up the nonlinear maps for high-dimensional data.
MAV urban localization from Google street view data
(2013)

We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2 km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.
Actinomycetes from the South China Sea sponges: isolation, diversity, and potential for aromatic polyketides discovery
(2015)

Marine sponges often harbor dense and diverse microbial communities including actinobacteria. To date no comprehensive investigation has been performed on the culturable diversity of the actinomycetes associated with South China Sea sponges. Structurally novel aromatic polyketides were recently discovered from marine sponge-derived Streptomyces and Saccharopolyspora strains, suggesting that sponge-associated actinomycetes can serve as a new source of aromatic polyketides. In this study, a total of 77 actinomycete strains were isolated from 15 South China Sea sponge species. Phylogenetic characterization of the isolates based on 16S rRNA gene sequencing supported their assignment to 12 families and 20 genera, among which three rare genera (Marihabitans, Polymorphospora, and Streptomonospora) were isolated from marine sponges for the first time. Subsequently, β-ketoacyl synthase (KSα) gene was used as marker for evaluating the potential of the actinomycete strains to produce aromatic polyketides. As a result, KSα gene was detected in 35 isolates related to seven genera (Kocuria, Micromonospora, Nocardia, Nocardiopsis, Saccharopolyspora, Salinispora, and Streptomyces). Finally, 10 strains were selected for small-scale fermentation, and one angucycline compound was detected from the culture extract of Streptomyces anulatus strain S71. This study advanced our knowledge of the sponge-associated actinomycetes regarding their diversity and potential in producing aromatic polyketides.
Fpga-based face detection system using Haar classifiers
(2009)

This paper presents a hardware architecture for face detection based system on AdaBoost algorithm using Haar features. We describe the hardware design techniques including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the processing speed of the face detection system. Also we discuss the optimization of the proposed architecture which can be scalable for configurable devices with variable resources. The proposed architecture for face detection has been designed using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured and compared with an equivalent software implementation. We show about 35 times increase of system performance over the equivalent software implementation.
Reduction of unbalanced axial magnetic force in post-fault operation of a novel six-phase double-stator axial flux PM machine using model predictrive control
(2016)

This paper investigated the post-fault operation of a novel six-phase double-stator axial flux permanent magnet machine with detached winding configuration, which was found to be superior to existing winding configuration in previous study. However, the unbalanced magnetic force problem still remains unsolved. In this paper, the axial force balancing control principle is proposed and a group of specific current waveforms are deduced. When applying these currents under post-fault condition, magnetic torque, axial magnetic force and rotor losses of the machine are calculated in finite element analysis. The results are compared with normal condition and commonly-used post-fault current waveforms. It is verified that this method reduced the unbalanced axial magnetic force immensely and the torque ripple was also kept at a low level. In order to achieve the proposed current waveform, finite control set model predictive control (FCS-MPC) is adopted. This paper proposed the post-fault model of dual three-phase permanent magnet machines and designed a cost function to track the desired current waveforms. The model of the machine is used to predict the future behavior of the controlled variables and the cost function decides the next step of the inverter by evaluating all the predictions. At last, it is verified by simulation results that the control strategy performs well in both dynamic and steady-state situations.
The Whale Optimization Algorithm
(2016)

This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html © 2016 Elsevier Ltd. All rights reserved.
Evolution strategies – A comprehensive introduction
(2002)

This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed.